1. Field of the Invention
The present invention relates to a method of and an apparatus for classifying patterns by use of a neural network. The present invention particularly relates to a method of and an apparatus for classifying patterns by use of a neural network applicable to a case where input patterns to be classified are possibly different from input patterns included in learning or training data or to a case where a structure of a problem is changed after a training or learning of a neural network and hence a pair of an input pattern to be classified and a correct output pattern associated with the input pattern may be in conflict with the contents of training or learning data for achieving corrections on the training data to conduct an appropriate classification of the patterns.
2. Description of the Prior Art
A conventional method of accomplishing a pattern classification for classifying input patterns into predetermined categories by use of a neural network has been described on page 39 of "Neural Network Information Processing" written by Hideki Asoh and published by the Sangyo Tosho Co., Ltd. in 1988. According to the description, information processing in a hierarchically layered network is a discrimination or transformation of patterns and a training or learning of a network means that, in response to a presented example of transformation, the network favorably imitates or simulates the transformation in an appropriate manner.
FIG. 1 schematically shows the principle of a neural network 11 in the prior art.
The neural network 11 is a computation model having a network form in which nodes 41, simulating nerve cells or neurons, are arranged in a hierarchy to be linked with each other by arcs 42. In this configuration, the arcs 42 are assigned weights to adjust the output power of each signal transmitted between the nodes 41. Moreover, each node 41 has a threshold value such that when a total of the input power received from the nodes 41 in a hierarchic layer preceding the layer of the pertinent node 41 is over the threshold, a signal is transmitted to the nodes 41 in the subsequent layer. Input pattern items of an input pattern 18 are supplied as state values of the respective nodes 41 in the first layer. According to the state values, it is possible to obtain the state values of the nodes in the second layer, in the third layer, and so on, thereby attaining output pattern items of an output pattern 19 as the state values of the final (output) layer of the network.
In FIG. 1, when the input pattern 18 includes m items or elements Xi (i=1, 2, . . . , m), the input pattern 18 can be represented as a vector X. EQU X=(X1, X2, . . . , Xm) (1)
Assume the input pattern 18 to be classified into any one of n categories. The output pattern 19, as the result of classification, is then expressed as a vector Y including n elements Yi (i=1, 2, . . . , n). EQU Y=(Y1, Y2, . . . , Yn) (2)
In this regard, when the input pattern 18 belongs to an i-th category, Yi=1 results; otherwise, Yi=0 results.
In the training of the neural network 11, the input pattern, namely, the vector X is supplied to the neural network 11 so as to cause the neural network 11 to compute the output pattern, namely, the vector Y. In this situation, a teacher pattern (correct output pattern) T is instructed as a desirable output pattern for the input pattern. Like the vector Y, the pattern T also includes n elements T1 to Tn. In response to the instructed pattern T, the neural network 11 corrects weights of the respective arcs to minimize the difference between the current output pattern Y and the desired output pattern T. The process including the indication of the transformation and the correction of weights is repeatedly achieved as above for all pairs of input patterns X and teacher patterns (correct output patterns) T. As a result, the neural network 11 simulates a favorable transformation from the input pattern X into the output pattern Y.